Vehicle breaking energy recovering method and apparatus

ABSTRACT

A vehicle braking energy recovering method includes obtaining current location information of a vehicle, determining a current road scenario based on the current location information of the vehicle, determining the current road scenario based on a mapping relationship between a road scenario and a weight, determining a safe distance and a safe speed of the vehicle based on the weight, determining a target torque based on the safe distance and the safe speed of the vehicle, and controlling, based on the target torque, a motor of the vehicle to recover braking energy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2016/112756, filed on Dec. 28, 2016, which claims priority toChinese Patent Application 201610988900.0, filed on Nov. 9, 2016. Thedisclosures of the aforementioned applications are hereby incorporatedby reference in their entireties.

TECHNICAL FIELD

This application relates to energy recovering technologies, inparticular, to a vehicle braking energy recovering method and apparatus.

BACKGROUND

At present, air pollution is serious, smog is frequent, and wideattention is paid to environment protection. Therefore, development ofelectric vehicles (EVs) has drawn attention to various countries.However, a driving range is a greatest problem that hinders EVpromotion.

Previously, a manner of improving EV energy utilization is usually usedto resolve the driving range problem. For example, braking energyrecovery may be used to improve EV energy utilization. FIG. 1 is aframework diagram of a vehicle braking energy recovery system in theprior art. As shown in FIG. 1, in existing EV braking energy recoveringand control decision, an accelerator signal, a braking signal, and aclutch signal are obtained through identification of opening degrees ofan acceleration pedal, a brake pedal, and a clutch pedal, and then basedon a maximum allowed charging current and a battery state of charge(SOC), an entire vehicle controller computes a brake torque commandbased on a motor rotational speed, and controls the motor to performenergy feedback.

However, the existing braking energy recovering manner is passivelyrecovering braking energy based on an operation performed by a driver ona pedal and statuses of a battery and a motor, resulting in a relativelylow braking energy recovery rate.

SUMMARY

Embodiments of this application provide a vehicle braking energyrecovering method and apparatus, to improve a braking energy recoveryrate.

According to a first aspect, an embodiment of this application providesa vehicle braking energy recovering method, where the method includesobtaining current location information of a vehicle, determining acurrent road scenario based on the current location information of thevehicle, determining, based on a mapping relationship between a roadscenario and a weight, a weight corresponding to the current roadscenario, determining a safe distance and a safe speed of the vehiclebased on the weight, determining a target torque based on the safedistance and the safe speed of the vehicle, and controlling, based onthe target torque, a motor of the vehicle to recover braking energy.

According to the vehicle braking energy recovering method provided inthe first aspect, the current location information of the vehicle isobtained, the current road scenario is determined based on the currentlocation information of the vehicle, the weight corresponding to thecurrent road scenario is determined based on the mapping relationshipbetween a road scenario and a weight, the safe distance and the safespeed of the vehicle are determined based on the weight, then the targettorque is determined based on the safe distance and the safe speed ofthe vehicle, and finally, the motor of the vehicle is controlled basedon the target torque to recover the braking energy. The safe speed andthe safe distance of the vehicle are determined based on the currentroad scenario of the vehicle, and then the target torque is determinedfor recovering the braking energy. In an embodiment, the road scenariois determined based on information perceived by the vehicle, anddifferent weights are assigned to different road scenarios to calculatethe safe distance and the safe speed, then determine a targetdeceleration, and allocate the torque, thereby improving a brakingenergy recovery rate.

In a possible design, the weight includes a safe distance weight and asafe speed weight, and the determining a safe distance and a safe speedof the vehicle based on the weight includes calculating the safedistance of the vehicle based on the safe distance weight of thevehicle, and calculating the safe speed of the vehicle based on the safespeed weight of the vehicle.

In the foregoing design, for a same scenario, the safe distance weightof the vehicle and the safe speed weight of the vehicle may be the same,or may be different.

In a possible design, before the calculating the safe distance of thevehicle based on the safe distance weight of the vehicle, the methodfurther includes obtaining information perceived by the vehicle, anddetermining a first safe distance L₁ based on the information perceivedby the vehicle, where L₁ is a relative distance between an obstacle andthe vehicle, and obtaining information transmitted by an Internet ofVehicles, and determining a second safe distance L₂ based on theinformation transmitted by the Internet of Vehicles, where L₂ is arelative distance between an obstacle and the vehicle, and thecalculating the safe distance of the vehicle based on the safe distanceweight of the vehicle includes calculating the safe distance of thevehicle according to a formula L_(safe)=δ_(1i)*L₁+δ_(2i)*L₂, whereL_(safe) is the safe distance, andthe safe distance weight includes twoparameters, δ_(1i) and δ_(2i).

In the foregoing design, the obstacle includes a moving or staticobject, for example, includes an object such as another vehicle or arailing.

In a possible design, before the calculating the safe speed of thevehicle based on the safe speed weight of the vehicle, the methodfurther includes obtaining information perceived by the vehicle, anddetermining a first safe speed V₁ based on the information perceived bythe vehicle, where V₁ is a relative speed between an obstacle and thevehicle, and obtaining information transmitted by an Internet ofVehicles, and determining a second safe speed V₂ based on theinformation transmitted by the Internet of Vehicles, where V₂ is arelative speed between an obstacle and the vehicle, and the calculatingthe safe distance of the vehicle based on the safe distance weight ofthe vehicle includes calculating the safe speed of the vehicle accordingto a formula V_(safe)=δ_(1i)*V₁+δ_(2i)*V₂, where V_(safe) is the safespeed, and the safe speed weight includes two parameters, δ_(1i) andδ_(2i).

In a possible design, before the calculating the safe distance of thevehicle based on the safe distance weight of the vehicle, the methodfurther includes obtaining information perceived by the vehicle, anddetermining a first safe distance L₁ based on the information perceivedby the vehicle, where L₁ is a relative distance between an obstacle andthe vehicle, obtaining information transmitted by an Internet ofVehicles, and determining a second safe distance L₂ based on theinformation transmitted by the Internet of Vehicles, where L₂ is arelative distance between an obstacle and the vehicle, and obtainingroad historical information that is stored in a cloud data center, anddetermining a third safe distance L₃ based on the road historicalinformation that is stored in the cloud data center, where L₃ is arelative distance between an obstacle and the vehicle, and thecalculating the safe distance of the vehicle based on the safe distanceweight of the vehicle includes calculating the safe distance of thevehicle according to a formula L_(safe)=δ_(1i)*L₁+δ_(2i)*L₂+δ_(3i)*L₃,where L_(safe) is the safe distance, and the safe distance weightincludes three parameters, δ_(1i), δ_(2i), and δ_(3i).

In a possible design, before the calculating the safe speed of thevehicle based on the safe speed weight of the vehicle, the methodfurther includes obtaining information perceived by the vehicle, anddetermining a first safe speed V₁ based on the information perceived bythe vehicle, where V₁ is a relative speed between an obstacle and thevehicle, obtaining information transmitted by an Internet of Vehicles,and determining a second safe speed V₂ based on the informationtransmitted by the Internet of Vehicles, where V₂ is a relative speedbetween the obstacle and the vehicle, and obtaining road historicalinformation that is stored in a cloud data center, and determining athird safe speed V₃ based on the road historical information that isstored in the cloud data center, where V₃ is a relative speed between anobstacle and the vehicle, and the calculating the safe speed of thevehicle based on the safe speed weight of the vehicle includescalculating the safe speed of the vehicle according to a formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂+δ_(3i)*V₃, where V_(safe) is the safespeed, and the safe speed weight includes three parameters, δ_(1i),δ_(2i), and δ_(3i).

According to the vehicle braking energy recovering method provided inthe foregoing possible designs, the safe distance and the safe speed ofthe vehicle may be calculated in different manners for different casessuch that manners of calculating the safe distance and the safe speedare more flexible.

In a possible design, the determining the target torque based on thesafe distance and the safe speed of the vehicle includes calculating atarget deceleration based on a driving speed of the vehicle, the safedistance, and the safe speed, and determining the target torque based onthe target deceleration.

In the foregoing possible design, the calculating a target decelerationbased on a driving speed of the vehicle, the safe distance, and the safespeed includes calculating the target deceleration according to thefollowing formula,

${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$where a_(trg) is the target deceleration, V_(safe) is the safe speed,L_(safe) is the safe distance, and v is the driving speed of thevehicle.

In the vehicle braking energy recovering method provided in theforegoing possible designs, the target deceleration is calculated basedon the driving speed of the vehicle, the safe distance, and the safespeed, and the target torque is determined based on the targetdeceleration and a vehicle dynamics model. In this way, the targettorque is calculated based on the target deceleration and by introducingthe vehicle dynamics model, and therefore an objective of recoveringbraking energy based on overall economic considerations can be achieved.

In a possible design, the information transmitted by the Internet ofVehicles includes at least one of a road-allowed maximum speed, a roadslope, a signal light status, a signal light distance, an averagetraffic speed of a current road, or a first congestion coefficient.

In a possible design, the road historical information that is stored inthe cloud data center includes an average vehicle speed in a currenttime period, an average vehicle distance in the current time period, anda second congestion coefficient, where the average vehicle speed in thecurrent time period, the average vehicle distance in the current timeperiod, and the second congestion coefficient are obtained throughcalculation by the cloud data center based on a machine learningalgorithm and historical data.

According to a second aspect, an embodiment of this application providesa vehicle braking energy recovering apparatus, where the apparatusincludes an obtaining module, configured to obtain current locationinformation of a vehicle, a determining module, configured to determinea current road scenario based on the current location information of thevehicle, where the determining module is further configured todetermine, based on a mapping relationship between a road scenario and aweight, a weight corresponding to the current road scenario, thedetermining module is further configured to determine a safe distanceand a safe speed of the vehicle based on the weight, and the determiningmodule is further configured to determine the target torque based on thesafe distance and the safe speed of the vehicle, and a control module,configured to control, based on the target torque, a motor of thevehicle to recover braking energy.

In a possible design, the weight includes a safe distance weight and asafe speed weight, and the determining module includes a firstcalculation unit, configured to calculate the safe distance of thevehicle based on the safe distance weight of the vehicle, and a secondcalculation unit, configured to calculate the safe speed of the vehiclebased on the safe speed weight of the vehicle.

In a possible design, the obtaining module is further configured toobtain information perceived by the vehicle, the determining module isfurther configured to determine a first safe distance L₁ based on theinformation perceived by the vehicle, where L₁ is a relative distancebetween an obstacle and the vehicle, the obtaining module is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module is further configured to determine a second safedistance L₂ based on the information transmitted by the Internet ofVehicles, where L₂ is a relative distance between the obstacle and thevehicle, and the first calculation unit is configured to calculate thesafe distance of the vehicle according to a formulaL_(safe)=δ_(1i)*L₁+δ_(2i)*L₂, where L_(safe) is the safe distance, andthe safe distance weight includes two parameters, δ_(1i) and δ_(2i).

In a possible design, the obtaining module is further configured toobtain information perceived by the vehicle, the determining module isfurther configured to determine a first safe speed V₁ based on theinformation perceived by the vehicle, where V₁ is a relative speedbetween an obstacle and the vehicle, the obtaining module is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module is further configured to determine a second safespeed V₂ based on the information transmitted by the Internet ofVehicles, where V₂ is a relative speed between an obstacle and thevehicle, and the second calculation unit is configured to calculate thesafe speed of the vehicle according to a formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂, where V_(safe) is the safe speed, and thesafe speed weight includes two parameters, δ_(1i) and δ_(2i).

In a possible design, the obtaining module is further configured toobtain information perceived by the vehicle, the determining module isfurther configured to determine a first safe distance L₁ based on theinformation perceived by the vehicle, where L₁ is a relative distancebetween an obstacle and the vehicle, the obtaining module is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module is further configured to determine a second safedistance L₂ based on the information transmitted by the Internet ofVehicles, where L₂ is a relative distance between an obstacle and thevehicle, the obtaining module is further configured to obtain roadhistorical information that is stored in a cloud data center, thedetermining module is further configured to determine a third safedistance L₃ based on the road historical information that is stored inthe cloud data center, where L₃ is a relative distance between anobstacle and the vehicle, and the first calculation unit is configuredto calculate the safe distance of the vehicle according to a formulaL_(safe)=δ_(1i)*L₁+δ_(2i)*L₂+δ_(3i)*L₃, where L_(safe) is the safedistance, and the safe distance weight includes three parameters,δ_(1i), δ_(2i), and δ_(3i).

In a possible design, the obtaining module is further configured toobtain information perceived by the vehicle, the determining module isfurther configured to determine a first safe speed V₁ based on theinformation perceived by the vehicle, where V₁ is a relative speedbetween an obstacle and the vehicle, the obtaining module is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module is further configured to determine a second safespeed V₂ based on the information transmitted by the Internet ofVehicles, where V₂ is a relative speed between the obstacle and thevehicle, the obtaining module is further configured to obtain roadhistorical information that is stored in a cloud data center, thedetermining module is further configured to determine a third safe speedV₃ based on the road historical information that is stored in the clouddata center, where V₃ is a relative speed between an obstacle and thevehicle, and the second calculation unit is configured to calculate thesafe speed of the vehicle according to a formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂+δ_(3i)*V₃, where V_(safe) is the safespeed, and the safe speed weight includes three parameters, δ_(1i),δ_(2i), and δ_(3i).

In a possible design, the determining module is configured to calculatea target deceleration based on a driving speed of the vehicle, the safedistance, and the safe speed, and determine the target torque based onthe target deceleration.

In a possible design, the determining module is further configured tocalculate the target deceleration according to the following formula,

${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$where a_(trg) is the target deceleration, V_(safe) is the safe speed,L_(safe) is the safe distance, and v is the driving speed of thevehicle.

In a possible design, the information transmitted by the Internet ofVehicles includes at least one of a road-allowed maximum speed, a roadslope, a signal light status, a signal light distance, an averagetraffic speed of a current road, or a first congestion coefficient.

In a possible design, the road historical information that is stored inthe cloud data center includes an average vehicle speed in a currenttime period, an average vehicle distance in the current time period, anda second congestion coefficient, where the average vehicle speed in thecurrent time period, the average vehicle distance in the current timeperiod, and the second congestion coefficient are obtained throughcalculation by the cloud data center based on a machine learningalgorithm and historical data.

For beneficial effects of the vehicle braking energy recoveringapparatus provided in the second aspect and the possible designs of thesecond aspect, refer to the beneficial effects brought by the firstaspect and the possible designs of the first aspect. Details are notdescribed herein again.

According to a third aspect, an embodiment of this application providesa vehicle, including a processor, configured to obtain current locationinformation of the vehicle, where the processor is further configured todetermine a current road scenario based on the current locationinformation of the vehicle, the processor is further configured todetermine, based on a mapping relationship between a road scenario and aweight, a weight corresponding to the current road scenario, theprocessor is further configured to determine a safe distance and a safespeed of the vehicle based on the weight, the processor is furtherconfigured to determine the target torque based on the safe distance andthe safe speed of the vehicle, and the processor is further configuredto control, based on the target torque, a motor of the vehicle torecover braking energy.

In a possible design, the weight includes a safe distance weight and asafe speed weight, the processor is further configured to calculate thesafe distance of the vehicle based on the safe distance weight of thevehicle, and the processor is further configured to calculate the safespeed of the vehicle based on the safe speed weight of the vehicle.

In a possible design, the processor is further configured to obtaininformation perceived by the vehicle, and determine a first safedistance L₁ based on the information perceived by the vehicle, where L₁is a relative distance between an obstacle and the vehicle, theprocessor is further configured to obtain information transmitted by anInternet of Vehicles, and determine a second safe distance L₂ based onthe information transmitted by the Internet of Vehicles, where L₂ is arelative distance between the obstacle and the vehicle, and theprocessor is further configured to calculate the safe distance of thevehicle according to a formula L_(safe)=δ_(1i)*L₁+δ_(2i)*L₂, whereL_(safe) is the safe distance, and the safe distance weight includes twoparameters, δ_(1i) and δ_(2i).

In a possible design, the processor is further configured to obtaininformation perceived by the vehicle, and determine a first safe speedV₁ based on the information perceived by the vehicle, where V₁ is arelative speed between an obstacle and the vehicle, the processor isfurther configured to obtain information transmitted by an Internet ofVehicles, and determine a second safe speed V₂ based on the informationtransmitted by the Internet of Vehicles, where V₂ is a relative speedbetween an obstacle and the vehicle, and the processor is furtherconfigured to calculate the safe speed of the vehicle according to aformula V_(safe)=δ_(1i)*V₁+δ_(2i)*V₂, where V_(safe) is the safe speed,and the safe speed includes two parameters, δ_(1i) and δ_(2i).

In a possible design, the processor is further configured to obtaininformation perceived by the vehicle, and determine a first safedistance L₁ based on the information perceived by the vehicle, where L₁is a relative distance between an obstacle and the vehicle, theprocessor is further configured to obtain information transmitted by anInternet of Vehicles, and determine a second safe distance L₂ based onthe information transmitted by the Internet of Vehicles, where L₂ is arelative distance between an obstacle and the vehicle, the processor isfurther configured to obtain road historical information that is storedin a cloud data center, and determine a third safe distance L₃ based onthe road historical information that is stored in the cloud data center,where L₃ is a relative distance between an obstacle and the vehicle, andthe processor is further configured to calculate the safe distance ofthe vehicle according to a formulaL_(safe)=δ_(1i)*L₁+δ_(2i)*L₂+δ_(3i)*L₃, where L_(safe) is the safedistance, and the safe distance weight includes three parameters,δ_(1i), δ_(2i), and δ_(3i).

In a possible design, the processor is further configured to obtaininformation perceived by the vehicle, and determine a first safe speedV₁ based on the information perceived by the vehicle, where V₁ is arelative speed between an obstacle and the vehicle, the processor isfurther configured to obtain information transmitted by an Internet ofVehicles, and determine a second safe speed V₂ based on the informationtransmitted by the Internet of Vehicles, where V₂ is a relative speedbetween the obstacle and the vehicle, the processor is furtherconfigured to obtain road historical information that is stored in acloud data center, and determine a third safe speed V₃ based on the roadhistorical information that is stored in the cloud data center, where V₃is a relative speed between an obstacle and the vehicle, and theprocessor is further configured to calculate the safe speed of thevehicle according to a formula V_(safe)=δ_(1i)*V₁+δ_(2i)*V₂+δ_(3i)*V₃,where V_(safe) is the safe speed, and the safe speed includes threeparameters, δ_(1i), δ_(2i), and δ_(3i).

In a possible design, the processor is further configured to calculate atarget deceleration based on a driving speed of the vehicle, the safedistance, and the safe speed, and the processor is further configured todetermine the target torque based on the target deceleration.

In a possible design, the processor is further configured to calculatethe target deceleration according to the following formula,

${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$where a_(trg) is the target deceleration, V_(safe) is the safe speed,L_(safe) is the safe distance, and v is the driving speed of thevehicle.

In a possible design, the information transmitted by the Internet ofVehicles includes at least one of a road-allowed maximum speed, a roadslope, a signal light status, a signal light distance, an averagetraffic speed of a current road, or a first congestion coefficient.

In a possible design, the road historical information that is stored inthe cloud data center includes an average vehicle speed in a currenttime period, an average vehicle distance in the current time period, anda second congestion coefficient, where the average vehicle speed in thecurrent time period, the average vehicle distance in the current timeperiod, and the second congestion coefficient are obtained throughcalculation by the cloud data center based on a machine learningalgorithm and historical data.

For beneficial effects of the vehicle provided in the third aspect andthe possible designs of the third aspect, refer to the beneficialeffects brought by the first aspect and the possible designs of thefirst aspect. Details are not described herein again.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a framework diagram of a vehicle braking energy recoverysystem.

FIG. 2 is a schematic system architectural diagram of a vehicle brakingenergy recovering method according to various embodiments of thedisclosure.

FIG. 3 is a schematic flowchart of Embodiment 1 of a vehicle brakingenergy recovering method according to various embodiments of thedisclosure.

FIG. 4 is a schematic flowchart 1 of calculating a safe distance of avehicle.

FIG. 5 is a schematic flowchart 2 of calculating a safe distance of avehicle.

FIG. 6 is a schematic flowchart 1 of calculating a safe speed of avehicle.

FIG. 7 is a schematic flowchart 2 of calculating a safe speed of avehicle.

FIG. 8 is a schematic structural diagram of Embodiment 1 of a vehiclebraking energy recovering apparatus according to various embodiments ofthe disclosure.

FIG. 9 is a schematic structural diagram of Embodiment 2 of a vehiclebraking energy recovering apparatus according to various embodiments ofthe disclosure.

FIG. 10 is a schematic structural diagram of an embodiment of a vehicleaccording to various embodiments of the disclosure.

DESCRIPTION OF EMBODIMENTS

FIG. 2 is a schematic system architectural diagram of a vehicle brakingenergy recovering method according to this application. As shown in FIG.2, an embodiment of this application is applicable to an Internet ofVehicles system. The system includes a perception subsystem, a computingand convergence subsystem, and an execution and decision subsystem.

The perception subsystem includes a self-perception unit, an Internet ofVehicles unit, a vehicle location positioning unit, a cloud data centerunit, and a driving intention identification unit. The self-perceptionunit is mainly a laser radar, a millimeter wave radar, a monocularcamera, a multi-view camera, an acceleration sensor, or the like that isequipped on an intelligent automobile and that measures aspeed/distance/direction of a vehicle. The Internet of Vehicles unit ismainly a module that obtains parameters of a surrounding environmentthrough vehicle-to-vehicle (V2V) information exchange,vehicle-to-infrastructure (V2I) information exchange, andvehicle-to-pedestrian (V2P) information exchange, and provides keyenvironment parameters for vehicle, road, and network coordination. Thevehicle location positioning unit mainly provides various types ofvehicle positioning information of, for example, the Global PositioningSystem( ), the Differential Global Positioning System (DGPS), the BEIDOUnavigation system, the GALILEO navigation system, Russia's GlobalnayaNavigazionnaya Sputnikovaya Sistema (GLONASS) navigation system, and aninertial measurement unit (IMU) system, and provides meter-levelpositioning precision, centimeter-level positioning precision, andpositioning precision of a lane-line positioning level. The cloud datacenter unit mainly obtains safe driving data of each road status indifferent periods under different weather conditions that is obtainedbased on big data, machine learning, and deep learning, for example, arisk grade and a congestion index. The driving intention identificationunit is configured to identify a driver's driving intention based on astatus of an accelerator pedal, a status of a brake pedal, and a vehiclecontrol status.

The computing and convergence subsystem mainly includes a vehicleenvironment perception information convergence unit, a vehicle targetdeceleration calculation unit, a vehicle target torque calculation unit,and a vehicle torque allocation and control unit. The vehicleenvironment perception information convergence unit is mainly configuredto integrate various parameters obtained by the perception subsystem,and select, based on a type of a vehicle driving scenario, weights ofthe various parameters as inputs to the vehicle target decelerationcalculation module.

The execution and decision subsystem includes an electric drive unit, apower battery unit, and an active mechanical braking unit.

According to the vehicle braking energy recovering method in thisembodiment of this application, two types of important informationinputs inside the vehicle (an electric drive system, a battery system, apedal status, a control status, and the like) and outside the vehicle(an environment, a road scenario, an external vehicle, and the like) arecomprehensively considered, to perform final regenerative brakingcontrol and drive control. Regenerative braking is optimized when adriver performs a corresponding braking operation, and moreover,regenerative braking is intelligently performed based on an environmentwhen the driver has no braking intention. This achieves overall economyof the vehicle and avoids sudden acceleration or sudden deceleration.Mechanical braking is used as least as possible such that optimalcontrol on a driving range is achieved.

The following embodiments describe in detail the technical solutions ofthis application. The following several embodiments may be combined witheach other, and a same or similar concept or process may not bedescribed repeatedly in some embodiments.

FIG. 3 is a schematic flowchart of Embodiment 1 of a vehicle brakingenergy recovering method according to this application. This embodimentof this application provides a vehicle braking energy recovering method.The method may be performed by any apparatus that performs a vehiclebraking energy recovering method, and the apparatus may be implementedusing software and/or hardware. In this embodiment, the apparatus may beintegrated into a vehicle.

Based on the system architecture shown in FIG. 2, as shown in FIG. 3,the method in this embodiment may include the following steps.

Step 301: Obtain current location information of a vehicle.

In this embodiment, the vehicle may obtain the current locationinformation of the vehicle using the vehicle location positioning unitof the perception subsystem. In an embodiment, the current locationinformation may be obtained using, for example, the GPS, the DGPS, theBeidou navigation system, the Galileo navigation system, the Russia'sGLONASS navigation system, or an IMU system.

Step 302: Determine a current road scenario based on the currentlocation information of the vehicle.

In this embodiment, the vehicle may obtain information such as a roadslope i₁ and a road speed limit V_(max1) in a first range using theself-perception unit of the perception subsystem, where the first rangemay be, for example, a maximum distance that can be measured by a radar,a camera, or a sensor, for example, 200 meters (m), may obtain aroad-allowed maximum speed V_(max2), a road slope i₂, a signal lightstatus S_(light), a distance L_(light) between the vehicle and a signallight, an average traffic speed V_(avg1) of a current road, and a firstcongestion coefficient f₁ using the Internet of Vehicles unit of theperception subsystem, and may obtain an average vehicle speed V_(avg2)on the current road in a current time period, a second congestioncoefficient f₂, and the like using the cloud data center unit based on alarge amount of data obtained using big data, machine learning, and thelike.

After obtaining the foregoing information and obtaining the currentlocation information of the vehicle, the vehicle determines the currentroad scenario based on road type identification information marked on amap. The road type identification information marked on the map includesG (national highways), S (provincial-level roads), X (county-levelroads), Y (township-level roads), and the like, or common roads (A-classroads), expressways (B-class roads), intelligent & connected drivingdedicated lanes (C-class roads), intelligent & connected drivingdedicated roads (D-class roads), and the like. In actual application,based on the foregoing information, the current location information ofthe vehicle, and the road type identification information marked on themap, the vehicle may determine the current road scenario of the vehicle.The road scenario includes an expressway (a scenario 1), aprovincial-level road (a scenario 2), an urban road (a scenario 3), atownship-level road (a scenario 4), or the like. For example, if thevehicle obtains a road slope of 30 degrees (°), a road-allowed maximumspeed of 30 kilometers (km) per hour (h) (km/h), and a first congestioncoefficient of 0.1, it indicates that, in an environment of the vehicle,the road slope is relatively large, the road-allowed maximum speed isrelatively low, and a vehicle congestion degree is not high. Based onthe current location information of the vehicle, the vehicle determinesthat a location of the vehicle is marked as Y (township-level roads) onthe map. In this case, it can be determined that the current roadscenario is a township-level road.

It should be noted that the vehicle may determine the current roadscenario based only on the current location information, or maydetermine the current road scenario based on parameters measured by atleast one of the self-perception unit, the Internet of Vehicles unit, orthe cloud data center unit and based on the current locationinformation. For a manner of determining the current road scenario, nolimitation is imposed herein in this embodiment.

Step 303: Determine, based on a mapping relationship between a roadscenario and a weight, a weight corresponding to the current roadscenario.

In this embodiment, the mapping relationship between a road scenario anda weight is prestored in the vehicle. After determining the current roadscenario, the vehicle determines, based on the prestored mappingrelationship, the weight corresponding to the current road scenario. Inan embodiment, the weight may be allocated based on a manner ofdetermining the current road scenario. For example, if the vehicledetermines the current road scenario based on parameters measured by theself-perception unit, the Internet of Vehicles unit, or the cloud datacenter unit and based on the current location information, weights aredetermined based on an allocation manner in Table 1.

TABLE 1 Scenario serial number δ1 (%) δ2 (%) δ3 (%) Scenario 1(expressway) 60 20 20 Scenario 2 (urban road) 70 30 0 Scenario 3(county-level road) 80 20 0 Scenario 4 (township-level road) 100 0 0

Customized configuration may be performed for δ1, δ2, and δ3. Forexample, generally in a vehicle sales region, customized configurationmay be performed based on traffic rules, map road working conditions,and the like in the sales region. In addition, for a pre-configuredmarket, configuration may be completed before a vehicle leaves afactory, for a post-configured market, configuration may be performedduring installation.

It should be noted that δ1, δ2, and δ3 are respectively percentages ofweights of data measured by the self-perception unit, the Internet ofVehicles unit, and the cloud data center unit. For example, for ascenario 1 (an expressway), percentages of weights of data measured bythe self-perception unit, the Internet of Vehicles unit, and the clouddata center unit are 60%, 20%, and 20%, respectively.

If the vehicle determines the current road scenario based on parametersmeasured by the self-perception unit and the Internet of Vehicles unitand based on the current location information, weights are determinedbased on an allocation manner in Table 2.

TABLE 2 Scenario serial number δ1 (%) δ2 (%) Scenario 1 (expressway) 8020 Scenario 2 (urban) 70 30 Scenario 3 (county-level road) 90 10Scenario 4 (township-level road) 100 0

In the scenario 1, the scenario 2, the scenario 3, and the scenario 4,distance parameters obtained by the self-perception system account forlargest proportions.

In addition, for a road condition of an expressway, δ2 related to a roadcondition of 200 meters to several kilometers, which has relativelylarge impact on a driving speed of the vehicle, and there is no trafficlight on the expressway, for an urban road, δ2 is impact of a trafficlight status and accounts for a relatively large proportion, and for atownship-level road, safe distance data obtained by the self-perceptionunit is relied on.

Step 304: Determine a safe distance and a safe speed of the vehiclebased on the weight.

In this embodiment, optionally, the weight includes a safe distanceweight and a safe speed weight. Therefore, in actual application, thedetermining a safe distance and a safe speed of the vehicle based on theweight includes calculating the safe distance of the vehicle based onthe safe distance weight of the vehicle, and calculating the safe speedof the vehicle based on the safe speed weight of the vehicle.

In addition, for a same scenario, the safe distance weight of thevehicle and the safe speed weight of the vehicle may be the same, or maybe different.

Optionally, implementable manners of calculating the safe distance ofthe vehicle based on the safe distance weight of the vehicle may includethe following types.

Manner 1: Referring to a schematic flowchart 1 of calculating a safedistance of a vehicle shown in FIG. 4, step 304 may include thefollowing steps.

Step 401: Obtain information perceived by the vehicle, and determine afirst safe distance L₁ based on the information perceived by thevehicle, where L₁ is a relative distance between an obstacle and thevehicle.

In an embodiment, the vehicle may obtain, using a radar, a camera,another type of sensor, or the like in the self-perception unit, theinformation perceived by the vehicle, and further determine the relativedistance L₁ between an obstacle and the vehicle, namely, the first safedistance L₁. The obstacle includes a moving or static object, forexample, includes an object such as another vehicle or a railing.

Step 402: Obtain information transmitted by an Internet of Vehicles, anddetermine a second safe distance L₂ based on the information transmittedby the Internet of Vehicles, where L₂ is a relative distance between anobstacle and the vehicle.

In an embodiment, the vehicle may obtain, through V2V, V2I, V2P, or thelike of the Internet of Vehicles unit, the information transmitted bythe Internet of Vehicles, and further determines the relative distanceL₂ between an obstacle and the vehicle, namely, the second safe distanceL₂. The obstacle may also include a moving or static object, forexample, include an object such as another vehicle or a railing.

Optionally, the information transmitted by the Internet of Vehiclesincludes at least one of a road-allowed maximum speed, a road slope, asignal light status, a signal light distance, an average traffic speedof a current road, and a first congestion coefficient. The firstcongestion coefficient may be represented by any value from 0 to 1. Alarger value indicates severer vehicle congestion.

Step 403: Calculate the safe distance of the vehicle according to aformula L_(safe)=δ_(1i)*L₁+δ_(2i)*L₂.

L_(safe) is the safe distance, and the safe distance weight includes twoparameters, and δ_(2i). In an embodiment, because information obtainedby the vehicle includes the information perceived by the vehicle and theinformation transmitted by the Internet of Vehicles, the safe distanceweight in the current road scenario may be determined based on themapping relationship in Table 2 in step 303. For example, the vehicleruns in the scenario 1 (an expressway). In this scenario, parameters ofthe safe distance weight are δ_(1i)=80% and δ_(2i)=20%, and arerepresented by (0.8 0.2). In addition, after the first safe distance L₁and the second safe distance L₂ are obtained, the safe distance of thevehicle is calculated according to the formulaL_(safe)=δ_(1i)*L₁+δ_(2i)*L₂.

Manner 2: Referring to a schematic flowchart 2 of calculating a safedistance of a vehicle shown in FIG. 5, step 304 may include thefollowing steps.

Step 501: Obtain information perceived by the vehicle, and determine afirst safe distance L₁ based on the information perceived by thevehicle, where L₁ is a relative distance between an obstacle and thevehicle.

Step 502: Obtain information transmitted by an Internet of Vehicles, anddetermine a second safe distance L₂ based on the information transmittedby the Internet of Vehicles, where L₂ is a relative distance between anobstacle and the vehicle.

Step 501 and step 502 are similar to step 401 and step 402, and detailsare not described herein again.

Step 503: Obtain road historical information that is stored in a clouddata center, and determine a third safe distance L₃ based on the roadhistorical information that is stored in the cloud data center, where L₃is a relative distance between an obstacle and the vehicle.

In an embodiment, the vehicle may obtain safe driving data in differenttime periods under different weather conditions using the cloud datacenter unit based on big data, machine learning, deep learning, or thelike, that is, obtain the relative distance L₃ between an obstacle andthe vehicle, namely, the third safe distance L₃.

Optionally, the road historical information that is stored in the clouddata center includes an average vehicle speed in a current time period,an average vehicle distance in the current time period, and a secondcongestion coefficient, where the average vehicle speed in the currenttime period, the average vehicle distance in the current time period,and the second congestion coefficient are obtained through calculationby the cloud data center based on a machine learning algorithm andhistorical data. The second congestion coefficient may be represented byany value from 0 to 1. A larger value indicates severer vehiclecongestion.

Step 504: Calculate the safe distance of the vehicle according to aformula L_(safe)=δ_(1i)*L₁+δ_(2i)*L₂+δ_(3i)*L₃.

L_(safe) is the safe distance, and the safe distance weight includesthree parameters, δ_(1i), δ_(2i), and δ_(3i). In an embodiment, becauseinformation obtained by the vehicle includes the information perceivedby the vehicle, the information transmitted by the Internet of Vehicles,and the road historical information that is stored in the cloud datacenter, the safe distance weight in the current road scenario may bedetermined based on the mapping relationship in Table 1 in step 303. Forexample, the vehicle runs in the scenario 2 (an expressway). In thisscenario, parameters of the safe distance weight are δ_(1i)=70%,δ_(2i)=30%, and δ_(3i)=0, and are represented by (0.7 0.3 0). Inaddition, after the first safe distance L₁, the second safe distance L₂,and the third safe distance L₃ are obtained, the safe distance of thevehicle is calculated according to the formulaL_(safe)=δ_(1i)*L₁+δ_(2i)*L₂+δ_(3i)*L₃.

Optionally, implementable manners of calculating the safe speed of thevehicle based on the safe speed weight of the vehicle may include thefollowing types.

Manner 1: Referring to a schematic flowchart 1 of calculating a safespeed of a vehicle shown in FIG. 6, step 304 may include the followingsteps.

Step 601: Obtain information perceived by the vehicle, and determine afirst safe speed V₁ based on the information perceived by the vehicle,where V₁ is a relative speed between an obstacle and the vehicle.

In an embodiment, the vehicle may obtain, using a radar, a camera,another type of sensor, or the like in the self-perception unit, theinformation perceived by the vehicle, and further determine the relativespeed V₁ between an obstacle and the vehicle, namely, the first safespeed V₁. The obstacle includes a moving or static object, for example,includes an object such as another vehicle or a railing.

Step 602: Obtain information transmitted by an Internet of Vehicles, anddetermine a second safe speed V₂ based on the information transmitted bythe Internet of Vehicles, where V₂ is a relative speed between anobstacle and the vehicle.

In an embodiment, the vehicle may obtain, through V2V, V2I, V2P, or thelike of the Internet of Vehicles unit, the information transmitted bythe Internet of Vehicles, and further determine the relative speed V₂between an obstacle and the vehicle, namely, the second safe speed V₂.The obstacle may also include a moving or static object, for example,include an object such as another vehicle or a railing.

Optionally, the information transmitted by the Internet of Vehiclesincludes at least one of a road-allowed maximum speed, a road slope, asignal light status, a signal light distance, an average traffic speedof a current road, and a first congestion coefficient. The firstcongestion coefficient may be represented by any value from 0 to 1. Alarger value indicates severer vehicle congestion.

Step 603: Calculate the safe speed of the vehicle according to a formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂.

V_(safe) is the safe speed, and the safe speed includes two parameters,δ_(1i) and δ_(2i). In an embodiment, because information obtained by thevehicle includes the information perceived by the vehicle and theinformation transmitted by the Internet of Vehicles, the safe speedweight in the current road scenario may be determined based on themapping relationship in Table 2 in step 303. For example, the vehicleruns in the scenario 1 (an expressway). In this scenario, parameters ofthe safe speed weight are δ_(1i)=80% and δ_(2i)=20%, and are representedby (0.8 0.2). In addition, after the first safe speed V₁ and the secondsafe speed V₂ are obtained, the safe speed of the vehicle is calculatedaccording to the formula V_(safe)=δ_(1i)*V₁+δ_(2i)+δ_(2i)*V₂.

Manner 2: Referring to a schematic flowchart 2 of calculating a safespeed of a vehicle shown in FIG. 7, step 304 may include the followingsteps.

Step 701: Obtain information perceived by the vehicle, and determine afirst safe speed V₁ based on the information perceived by the vehicle,where V₁ is a relative speed between an obstacle and the vehicle.

Step 702: Obtain information transmitted by an Internet of Vehicles, anddetermine a second safe speed V₂ based on the information transmitted bythe Internet of Vehicles, where V₂ is a relative speed between anobstacle and the vehicle.

Step 701 and step 702 are similar to step 601 and step 602, and detailsare not described herein again.

Step 703: Obtain road historical information that is stored in a clouddata center, and determine a third safe speed V₃ based on the roadhistorical information that is stored in the cloud data center, where V₃is a relative speed between an obstacle and the vehicle.

In an embodiment, the vehicle may obtain safe driving data in differenttime periods under different weather conditions using the cloud datacenter unit based on big data, machine learning, deep learning, or thelike, that is, obtain the relative speed V₃ between an obstacle and thevehicle, namely, the third safe speed V₃.

Optionally, the road historical information that is stored in the clouddata center includes an average vehicle speed in a current time period,an average vehicle distance in the current time period, and a secondcongestion coefficient, where the average vehicle speed in the currenttime period, the average vehicle distance in the current time period,and the second congestion coefficient are obtained through calculationby the cloud data center based on a machine learning algorithm andhistorical data. The second congestion coefficient may be represented byany value from 0 to 1. A larger value indicates severer vehiclecongestion.

Step 704: Calculate the safe speed of the vehicle according to a formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂+δ_(3i)*V₃.

V_(safe) is the safe speed, and the safe speed includes threeparameters, δ_(1i), δ_(2i), and δ_(3i). In an embodiment, becauseinformation obtained by the vehicle includes the information perceivedby the vehicle, the information transmitted by the Internet of Vehicles,and the road historical information that is stored in the cloud datacenter, the safe speed weight in the current road scenario may bedetermined based on the mapping relationship in Table 1 in step 303. Forexample, the vehicle runs in the scenario 2 (an expressway). In thisscenario, parameters of the safe speed weight are δ_(1i)=70%,δ_(2i)=30%, and δ_(3i)=0, and are represented by (0.7, 0.3, and 0,respectively). In addition, after the first safe speed V₁, the secondsafe speed V₂, and the third safe speed V₃ are obtained, the safedistance of the vehicle is calculated according to the formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂+δ_(3i)*V₃.

Step 305: Determine a target torque based on the safe distance and thesafe speed of the vehicle.

In this embodiment, after the safe distance and the safe speed of thevehicle are calculated, the target torque is determined based on thecalculated safe distance and safe speed such that the vehicledetermines, based on the target torque and a driving intention of adriver, a torque required by the entire vehicle to control a motor ofthe vehicle to recover braking energy.

Optionally, the determining a target torque based on the safe distanceand the safe speed of the vehicle includes calculating a targetdeceleration based on a driving speed of the vehicle, the safe distance,and the safe speed, and determining the target torque based on thetarget deceleration.

In an embodiment, the vehicle target deceleration calculation unit inthe computing and convergence subsystem may calculate the targetdeceleration according to a formula

${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$where a_(trg) is the target deceleration, V_(safe) is the safe speed,L_(safe) is the safe distance, and v is the driving speed of thevehicle.

After the target deceleration is determined, the vehicle target torquecalculation unit calculates the target torque based on the targetdeceleration, a road slope i obtained by the perception subsystem, and avehicle dynamics model. In an embodiment, the target torque may becalculated according to a formula

$\frac{( {{Gf} + {\frac{C_{D}A}{21.15}v^{2}} + {Gi} + {\delta\;{ma}_{trg}}} )r}{i_{g}i_{0}\eta_{T}} = {T_{trg}.}$G is a weight of the vehicle, f is a rolling resistance coefficient,C_(D) is a wind resistance coefficient, A is a frontal area of thevehicle, v is a real-time driving speed of the vehicle, i is a slope ofa road on which the vehicle runs, δ is a rotational mass conversioncoefficient, m is a mass of the vehicle, a_(trg) is the targetdeceleration, r is a radius of a wheel of the vehicle, i_(g) is atransmission gear ratio of the vehicle, i₀ is a final drive ratio, η_(T)is a mechanical transmission efficiency, and T_(trg) is the targettorque.

When T_(trg) is greater than 0, T_(trg) is a target driving torque, orwhen T_(trg) is less than 0, T_(trg) is a target braking torque.

In this embodiment, the target torque is calculated by introducing thevehicle dynamics model to achieve an objective of recovering brakingenergy based on overall economic considerations.

Step 306: Control, based on the target torque, a motor of the vehicle torecover braking energy.

In this embodiment, after determining the target torque, the vehicledetermines, based on the target torque and a driving intention that isidentified by a driving intention identification unit, the torquerequired by the entire vehicle, and allocates the determined torquerequired by the entire vehicle, to control the motor of the vehicle torecover the braking energy.

In an embodiment, the vehicle torque allocation and control unitallocates the torque based on a maximum output torque value of the motorin the vehicle, a minimum output torque value of the motor in thevehicle, real-time allowable charging power of a battery in the vehicle,and real-time allowable discharging power of a battery in the vehicle tocontrol the motor and an active mechanical braking system to work.

According to the vehicle braking energy recovering method provided inthis embodiment of the this application, the current locationinformation of the vehicle is obtained, the current road scenario isdetermined based on the current location information of the vehicle, theweight corresponding to the current road scenario is determined based onthe mapping relationship between a road scenario and a weight, the safedistance and the safe speed of the vehicle are determined based on theweight, then the target torque is determined based on the safe distanceand the safe speed of the vehicle, and finally, the motor of the vehicleis controlled based on the target torque to recover the braking energy.The safe speed and the safe distance of the vehicle are determined basedon the current road scenario of the vehicle, and then the target torqueis determined for recovering the braking energy. In an embodiment, theroad scenario is determined based on information perceived by thevehicle, and different weights are assigned to different road scenariosto calculate the safe distance and the safe speed, then determine thetarget deceleration, and allocate the torque, thereby improving abraking energy recovery rate.

FIG. 8 is a schematic structural diagram of Embodiment 1 of a vehiclebraking energy recovering apparatus according to an embodiment of thisapplication. The recovering apparatus may be an independent vehicle, ormay be an apparatus integrated into a vehicle. The apparatus may beimplemented using software, hardware, or a combination of software andhardware. As shown in FIG. 8, the recovering apparatus includes anobtaining module 11, configured to obtain current location informationof a vehicle, a determining module 12, configured to determine a currentroad scenario based on the current location information of the vehicle,where the determining module 12 is further configured to determine,based on a mapping relationship between a road scenario and a weight, aweight corresponding to the current road scenario, the determiningmodule 12 is further configured to determine a safe distance and a safespeed of the vehicle based on the weight, and the determining module 12is further configured to determine the target torque based on the safedistance and the safe speed of the vehicle, and a control module 13,configured to control, based on the target torque, a motor of thevehicle to recover braking energy.

Optionally, the obtaining module 11, the determining module 12, and thecontrol module 13 may correspond to a processor in the vehicle.

The vehicle braking energy recovering apparatus provided in thisembodiment of this application may perform the foregoing methodembodiments. Implementation principles and technical effects of theapparatus are similar to those of the method embodiments, and detailsare not described herein again.

FIG. 9 is a schematic structural diagram of Embodiment 2 of a vehiclebraking energy recovering apparatus according to an embodiment of thisapplication. Based on the foregoing embodiment, further, the weightincludes a safe distance weight and a safe speed weight, and thedetermining module 12 includes a first calculation unit 121, configuredto calculate the safe distance of the vehicle based on the safe distanceweight of the vehicle, and a second calculation unit 122, configured tocalculate the safe speed of the vehicle based on the safe speed weightof the vehicle.

Optionally, the obtaining module 11 is further configured to obtaininformation perceived by the vehicle, the determining module 12 isfurther configured to determine a first safe distance L₁ based on theinformation perceived by the vehicle, where L₁ is a relative distancebetween an obstacle and the vehicle, the obtaining module 11 is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module 12 is further configured to determine a secondsafe distance L₂ based on the information transmitted by the Internet ofVehicles, where L₂ is a relative distance between the obstacle and thevehicle, and the first calculation unit 121 is configured to calculatethe safe distance of the vehicle according to a formulaL_(safe)=δ_(1i)*L₁+δ_(2i)*L₂, where L_(safe) is the safe distance, andthe safe distance weight includes two parameters, δ_(1i) and δ_(2i).

Optionally, the obtaining module 11 is further configured to obtaininformation perceived by the vehicle, the determining module 12 isfurther configured to determine a first safe speed V₁ based on theinformation perceived by the vehicle, where V₁ is a relative speedbetween an obstacle and the vehicle, the obtaining module 11 is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module 12 is further configured to determine a secondsafe speed V₂ based on the information transmitted by the Internet ofVehicles, where V₂ is a relative speed between an obstacle and thevehicle, and the second calculation unit 122 is configured to calculatethe safe speed of the vehicle according to a formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂, where V_(safe) is the safe speed, and thesafe speed includes two parameters, δ_(1i) and δ_(2i).

Optionally, the obtaining module 11 is further configured to obtaininformation perceived by the vehicle, the determining module 12 isfurther configured to determine a first safe distance L₁ based on theinformation perceived by the vehicle, where L₁ is a relative distancebetween an obstacle and the vehicle, the obtaining module 11 is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module 12 is further configured to determine a secondsafe distance L₂ based on the information transmitted by the Internet ofVehicles, where L₂ is a relative distance between an obstacle and thevehicle, the obtaining module 11 is further configured to obtain roadhistorical information that is stored in a cloud data center, thedetermining module 12 is further configured to determine a third safedistance L₃ based on the road historical information that is stored inthe cloud data center, where L₃ is a relative distance between anobstacle and the vehicle, and the first calculation unit 121 isconfigured to calculate the safe distance of the vehicle according to aformula L_(safe)=δ_(1i)*L₁+δ_(2i)*L₂+δ_(3i)*L₃, where L_(safe) is thesafe distance, and the safe distance weight includes three parameters,δ_(1i), δ_(2i), and δ_(3i).

Optionally, the obtaining module 11 is further configured to obtaininformation perceived by the vehicle, the determining module 12 isfurther configured to determine a first safe speed V₁ based on theinformation perceived by the vehicle, where V₁ is a relative speedbetween an obstacle and the vehicle, the obtaining module 11 is furtherconfigured to obtain information transmitted by an Internet of Vehicles,the determining module 12 is further configured to determine a secondsafe speed V₂ based on the information transmitted by the Internet ofVehicles, where V₂ is a relative speed between the obstacle and thevehicle, the obtaining module 11 is further configured to obtain roadhistorical information that is stored in a cloud data center, thedetermining module 12 is further configured to determine a third safespeed V₃ based on the road historical information that is stored in thecloud data center, where V₃ is a relative speed between an obstacle andthe vehicle, and the second calculation unit 122 is configured tocalculate the safe speed of the vehicle according to a formulaV_(safe)=δ_(1i)*V₁+δ_(2i)*V₂+δ_(3i)*V₃, where V_(safe) is the safespeed, and the safe speed includes three parameters, δ_(1i), δ_(2i), andδ_(3i).

Optionally, the determining module 12 is configured to calculate atarget deceleration based on a driving speed of the vehicle, the safedistance, and the safe speed, and determine the target torque based onthe target deceleration.

Optionally, the determining module 12 is further configured to calculatethe target deceleration according to the following formula,

${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$where a_(trg) is the target deceleration, V_(safe) is the safe speed,L_(safe) is the safe distance, and v is the driving speed of thevehicle.

Optionally, the information transmitted by the Internet of Vehiclesincludes at least one of a road-allowed maximum speed, a road slope, asignal light status, a signal light distance, an average traffic speedof a current road, or a first congestion coefficient.

Optionally, the road historical information that is stored in the clouddata center includes an average vehicle speed in a current time period,an average vehicle distance in the current time period, and a secondcongestion coefficient, where the average vehicle speed in the currenttime period, the average vehicle distance in the current time period,and the second congestion coefficient are obtained through calculationby the cloud data center based on a machine learning algorithm andhistorical data.

The vehicle braking energy recovering apparatus provided in thisembodiment of this application may perform the foregoing methodembodiments. Implementation principles and technical effects of theapparatus are similar to those of the method embodiments, and detailsare not described herein again.

FIG. 10 is a schematic structural diagram of an embodiment of a vehicleaccording to this application. As shown in FIG. 10, the vehicle mayinclude a sender 20, a processor 21, a memory 22, and at least onecommunications bus 23. The communications bus 23 is configured toimplement communication connection between elements. The memory 22 mayinclude a high-speed random access memory (RAM) memory, or may furtherinclude a nonvolatile memory (NVM), for example, at least one magneticdisk storage. In the memory 22, various programs may be stored and usedto complete various processing functions and implement the steps of themethod in the embodiments. In addition, the vehicle may further includea receiver 24. The receiver 24 in this embodiment may be a correspondinginput interface that has a communications function and an informationreceiving function. The sender 20 in this embodiment may be acorresponding output interface that has a communications function and aninformation sending function. Optionally, the sender 20 and the receiver24 may be integrated into one communications interface, or may be twoindependent communications interfaces.

In this embodiment, the processor 21 is configured to obtain currentlocation information of the vehicle, the processor 21 is furtherconfigured to determine a current road scenario based on the currentlocation information of the vehicle, the processor 21 is furtherconfigured to determine, based on a mapping relationship between a roadscenario and a weight, a weight corresponding to the current roadscenario, the processor 21 is further configured to determine a safedistance and a safe speed of the vehicle based on the weight, theprocessor 21 is further configured to determine the target torque basedon the safe distance and the safe speed of the vehicle, and theprocessor 21 is further configured to control, based on the targettorque, a motor of the vehicle to recover braking energy.

Optionally, the weight includes a safe distance weight and a safe speedweight, the processor 21 is further configured to calculate the safedistance of the vehicle based on the safe distance weight of thevehicle, and the processor 21 is further configured to calculate thesafe speed of the vehicle based on the safe speed weight of the vehicle.

Optionally, the processor 21 is further configured to obtain informationperceived by the vehicle, and determine a first safe distance L₁ basedon the information perceived by the vehicle, where L₁ is a relativedistance between an obstacle and the vehicle, the processor 21 isfurther configured to obtain information transmitted by an Internet ofVehicles, and determine a second safe distance L₂ based on theinformation transmitted by the Internet of Vehicles, where L₂ is arelative distance between the obstacle and the vehicle, and theprocessor 21 is further configured to calculate the safe distance of thevehicle according to a formula L_(safe)=δ_(1i)*L₁+δ_(2i)*L₂, whereL_(safe) is the safe distance, and the safe distance weight includes twoparameters, δ_(1i) and δ_(2i).

Optionally, the processor 21 is further configured to obtain informationperceived by the vehicle, and determine a first safe speed V₁ based onthe information perceived by the vehicle, where V₁ is a relative speedbetween an obstacle and the vehicle, the processor 21 is furtherconfigured to obtain information transmitted by an Internet of Vehicles,and determine a second safe speed V₂ based on the informationtransmitted by the Internet of Vehicles, where V₂ is a relative speedbetween an obstacle and the vehicle, and the processor 21 is furtherconfigured to calculate the safe speed of the vehicle according to aformula V_(safe)=δ_(1i)*V₁+δ_(2i)*V₂, where V_(safe) is the safe speed,and the safe speed includes two parameters, δ_(1i) and δ_(2i).

Optionally, the processor 21 is further configured to obtain informationperceived by the vehicle, and determine a first safe distance L₁ basedon the information perceived by the vehicle, where L₁ is a relativedistance between an obstacle and the vehicle, the processor 21 isfurther configured to obtain information transmitted by an Internet ofVehicles, and determine a second safe distance L₂ based on theinformation transmitted by the Internet of Vehicles, where L₂ is arelative distance between an obstacle and the vehicle, the processor 21is further configured to obtain road historical information that isstored in a cloud data center, and determine a third safe distance L₃based on the road historical information that is stored in the clouddata center, where L₃ is a relative distance between an obstacle and thevehicle, and the processor 21 is further configured to calculate thesafe distance of the vehicle according to a formulaL_(safe)=δ_(1i)*L₁+δ_(2i)*L₂+δ_(3i)*L₃, where L_(safe) is the safedistance, and the safe distance weight includes three parameters,δ_(1i), δ_(2i), and δ_(3i).

Optionally, the processor 21 is further configured to obtain informationperceived by the vehicle, and determine a first safe speed V₁ based onthe information perceived by the vehicle, where V₁ is a relative speedbetween an obstacle and the vehicle, the processor 21 is furtherconfigured to obtain information transmitted by an Internet of Vehicles,and determine a second safe speed V₂ based on the informationtransmitted by the Internet of Vehicles, where V₂ is a relative speedbetween the obstacle and the vehicle, the processor 21 is furtherconfigured to obtain road historical information that is stored in acloud data center, and determine a third safe speed V₃ based on the roadhistorical information that is stored in the cloud data center, where V₃is a relative distance between an obstacle and the vehicle, and theprocessor 21 is further configured to calculate the safe speed of thevehicle according to a formula V_(safe)=δ_(1i)*V₁+δ_(2i)*V₂+δ_(3i)*V₃,where V_(safe) is the safe speed, and the safe speed includes threeparameters, δ_(1i), δ_(2i), and δ_(3i).

Optionally, the processor 21 is further configured to calculate a targetdeceleration based on a driving speed of the vehicle, the safe distance,and the safe speed, and the processor 21 is further configured todetermine the target torque based on the target deceleration.

Optionally, the processor 21 is further configured to calculate thetarget deceleration according to the following formula,

${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$where a_(trg) is the target deceleration, V_(safe) is the safe speed,L_(safe) is the safe distance, and v is the driving speed of thevehicle.

Optionally, the information transmitted by the Internet of Vehiclesincludes at least one of a road-allowed maximum speed, a road slope, asignal light status, a signal light distance, an average traffic speedof a current road, or a first congestion coefficient.

Optionally, the road historical information that is stored in the clouddata center includes an average vehicle speed in a current time period,an average vehicle distance in the current time period, and a secondcongestion coefficient, where the average vehicle speed in the currenttime period, the average vehicle distance in the current time period,and the second congestion coefficient are obtained through calculationby the cloud data center based on a machine learning algorithm andhistorical data.

The vehicle provided in this embodiment of this application may performthe foregoing method embodiments. Implementation principles andtechnical effects of the vehicle are similar to those of the methodembodiments, and details are not described herein again.

It may be clearly understood by a person skilled in the art that, forthe purpose of convenient and brief description, division of theforegoing function modules is merely used as an example forillustration. In actual application, the foregoing functions can beallocated to different function modules and computing hardware to beimplemented based on a requirement, that is, an inner structure of theapparatus is divided into different function modules to implement all orsome of the functions described above. For a detailed working process ofthe foregoing system, apparatus, and unit, reference may be made to acorresponding process in the foregoing method embodiments, and detailsare not described herein again.

In the several embodiments provided in this application, it should beunderstood that the disclosed system, apparatus, and method may beimplemented in other manners. For example, the described apparatusembodiment is merely an example. For example, the module or unitdivision is merely logical function division and may be other divisionin actual implementation. For example, a plurality of units orcomponents may be combined or integrated into another system, or somefeatures may be ignored or not performed. In addition, the displayed ordiscussed mutual couplings or direct couplings or communicationconnections may be implemented using some interfaces. The indirectcouplings or communication connections between the apparatuses or unitsmay be implemented in electrical, mechanical, or other forms.

The units described as separate parts may or may not be physicallyseparate, and parts displayed as units may or may not be physical units,may be located in one position, or may be distributed on a plurality ofnetwork units. Some or all of the units may be selected based on actualrequirements to achieve the objectives of the solutions of theembodiments.

In addition, functional units in the embodiments of this application maybe integrated into one processing unit, or each of the units may existalone physically, or two or more units are integrated into one unit. Theintegrated unit may be implemented in any combination of hardware,software, or firmware.

In an embodiment, the integrated unit may be stored in a non-transitorycomputer-readable storage medium. In this embodiment, severalinstructions for instructing a computer device (which may be a personalcomputer, a server, a network device, or the like) or a processor toperform all or a part of the steps of the methods described in theembodiments of this application are stored in a memory. The foregoingstorage medium includes any medium that can store program code, such asa USB flash drive, a removable hard disk, a read-only memory (ROM), aRAM, a magnetic disk, or an optical disc.

What is claimed is:
 1. A vehicle braking energy recovering method,comprising: obtaining information perceived by a vehicle; determining afirst distance based on the information perceived by the vehicle,wherein the first distance is a relative distance between a firstobstacle and the vehicle; obtaining information from an Internet ofVehicles; determining a second distance based on the information fromthe Internet of Vehicles, wherein the second distance is a secondrelative distance between a second obstacle and the vehicle; determininga third distance based on the first distance and the second distance;determining a target torque of the vehicle based on the third distance;and controlling a motor of the vehicle to recover braking energy basedon the target torque.
 2. The method according to claim 1, wherein themethod further comprises: obtaining road historical information from acloud data center; and determining a fourth distance based on the roadhistorical information, wherein the fourth distance is a relativedistance between a third obstacle and the vehicle, and whereindetermining the third distance comprises determining the third distancebased on the first distance, the second distance, and the fourthdistance.
 3. The method according to claim 1, further comprising:determining a first speed based on the information perceived by thevehicle, wherein the first speed is a relative speed between the firstobstacle and the vehicle; determining a second speed based on theinformation from the Internet of Vehicles, the second speed is arelative speed between the second obstacle and the vehicle; anddetermining a third speed based on the first speed and the second speed,and wherein the determining the target torque comprises determining thetarget torque based on the third distance and the third speed.
 4. Themethod according to claim 3, wherein the method further comprises:obtaining road historical information from a cloud data center; anddetermining a fourth speed based on the road historical information,wherein the fourth speed is a relative speed between a third obstacleand the vehicle, and wherein determining the third speed comprisesdetermining the third speed based on the first speed, the second speed,and the fourth speed.
 5. The method according to claim 3, whereindetermining the target torque comprises: calculating a targetdeceleration based on a driving speed of the vehicle, the thirddistance, and the third speed; and determining the target torque basedon the target deceleration.
 6. The method according to claim 5, whereincalculating the target deceleration comprises calculating the targetdeceleration according to the following formula,${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$ wherein a_(trg) isthe target deceleration, V_(safe) is the third speed, L_(safe) is thethird distance, and v is the driving speed of the vehicle.
 7. The methodaccording to claim 3, wherein the method further comprises: obtainingcurrent location information of the vehicle; determining a current roadscenario based on the current location information of the vehicle;determining a speed weight corresponding to the current road scenariobased on a mapping relationship between the current road scenario andthe speed weight, wherein determining the third speed comprisesdetermining the third speed based on the first speed, the second speed,and the speed weight.
 8. The method according to claim 1, furthercomprising: obtaining current location information of the vehicle;determining a current road scenario based on the current locationinformation of the vehicle; and determining a distance weightcorresponding to the current road scenario based on a mappingrelationship between the current road scenario and the distance weight,wherein determining the third distance comprises determining the thirddistance based on the first distance, the second distance, and thedistance weight.
 9. A vehicle braking energy recovering apparatus,wherein the apparatus comprises: a memory configured to storeinstructions; and a processor coupled to the memory and configured toexecute the instructions, which when executed, cause the processor to beconfigured to: obtain information perceived by a vehicle; determine afirst distance based on the information perceived by the vehicle,wherein the first distance is a relative distance between a firstobstacle and the vehicle; obtain information from an Internet ofVehicles; determine a second distance based on the information from theInternet of Vehicles, wherein the second distance is a second relativedistance between a second obstacle and the vehicle; determine a thirddistance based on the first distance and the second distance; determinea target torque of the vehicle based on the third distance; and controla motor of the vehicle to recover braking energy based on the targettorque.
 10. The apparatus according to claim 9, wherein theinstructions, when executed by the processor, further cause theprocessor to be configured to: obtain road historical information from acloud data center; determine a fourth distance based on the roadhistorical information, wherein the fourth distance is a relativedistance between a third obstacle and the vehicle, and whereindetermining the third distance comprises determining the third distancebased on the first distance, the second distance, and the fourthdistance.
 11. The apparatus according to claim 10, wherein the roadhistorical information comprises at least one of an average vehiclespeed in a current time period, an average vehicle distance in thecurrent time period, or a second congestion coefficient.
 12. Theapparatus according to claim 9, wherein the instructions, when executedby the processor, further cause the processor to be configured to:determine a first speed based on the information perceived by thevehicle, wherein the first speed is a relative speed between the firstobstacle and the vehicle; determine a second speed based on theinformation from the Internet of Vehicles, the second speed is arelative speed between the second obstacle and the vehicle; determine athird speed based on the first speed and the second speed; and determinethe target torque based on the third distance and the third speed. 13.The apparatus according to claim 12, wherein the instructions, whenexecuted by the processor, further cause the processor to be configuredto: obtain road historical information from a cloud data center;determine a fourth speed based on the road historical information,wherein the fourth speed is a relative speed between a third obstacleand the vehicle, and wherein determining the third speed comprisesdetermining the third speed based on the first speed, the second speed,and the fourth speed.
 14. The apparatus according to claim 12, whereinthe instructions, when executed by the processor, further cause theprocessor to be configured to: calculate a target deceleration based ona driving speed of the vehicle, the third distance, and the third speed;and determine the target torque based on the target deceleration. 15.The apparatus according to claim 14, the instructions, when executed bythe processor, further cause the processor to be configured to calculatethe target deceleration according to the following formula${a_{trg} = \frac{v_{safe}^{2} - v^{2}}{2L_{safe}}},$ wherein a_(trg) isthe target deceleration, V_(safe) is the third speed, L_(safe) is thethird distance, and v is the driving speed of the vehicle.
 16. Theapparatus according to claim 12, wherein the instructions, when executedby the processor, further cause the processor to be configured to:obtain current location information of the vehicle; determine a currentroad scenario based on the current location information of the vehicle;determine speed weight corresponding to the current road scenario basedon a mapping relationship between the current road scenario and thespeed weight; and determine the third speed based on the first speed,the second speed, and the speed weight.
 17. The apparatus according toclaim 9, wherein the instructions, when executed by the processor,further cause the processor to be configured to: obtain current locationinformation of the vehicle; determine a current road scenario based onthe current location information of the vehicle; determine a distanceweight corresponding to the current road scenario based on a mappingrelationship between the current road scenario and the distance weight;and determine the third distance based on the first distance, the seconddistance, and the distance weight.
 18. The apparatus according to claim9, wherein the information from the Internet of Vehicles comprises atleast one of a road-allowed maximum speed, a road slope, a signal lightstatus, a signal light distance, an average traffic speed of a currentroad, or a first congestion coefficient.
 19. A non-transitorycomputer-readable storage medium storing programming instructions forexecution by at least one processor, the programming instructionsinstructing the at least one processor to: obtain information perceivedby a vehicle; determine a first distance based on the informationperceived by the vehicle, wherein the first distance is a relativedistance between a first obstacle and the vehicle; obtain informationfrom an Internet of Vehicles; determine a second distance based on theinformation from the Internet of Vehicles, wherein the second distanceis a second relative distance between a second obstacle and the vehicle;determine a third distance based on the first distance and the seconddistance; determine the target torque of the vehicle based on the thirddistance; and control a motor of the vehicle to recover braking energybased on the target torque.